{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-11-25T10:20:40.347175Z",
     "start_time": "2025-11-25T10:20:39.911620Z"
    }
   },
   "source": [
    "# 一、 1. 数据加载和预处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('housing_data.csv')\n",
    "\n",
    "# 检查数据基本信息\n",
    "print(\"数据形状:\", data.shape)\n",
    "print(\"\\n数据前5行:\")\n",
    "print(data.head())\n",
    "print(\"\\n数据描述性统计:\")\n",
    "print(data.describe())\n",
    "print(\"\\n缺失值检查:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# 检查异常值（age列有负值）\n",
    "print(\"\\nAge列最小值:\", data['age'].min())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (100, 7)\n",
      "\n",
      "数据前5行:\n",
      "         size  bedrooms  bathrooms        age  garage_size  near_school  \\\n",
      "0  134.901425  4.990343   4.130858   8.368040     1.836736            1   \n",
      "1  115.852071  5.106719   3.906016  10.518552     2.139510            1   \n",
      "2  139.430656  5.617256   4.416375  20.978349     2.499251            1   \n",
      "3  165.690896  5.912679   4.801504  19.882962     2.647247            0   \n",
      "4  112.975399  5.178865   3.281330  14.832787     2.184851            1   \n",
      "\n",
      "        price  \n",
      "0  342.987204  \n",
      "1  318.054018  \n",
      "2  360.693196  \n",
      "3  374.684653  \n",
      "4  295.793720  \n",
      "\n",
      "数据描述性统计:\n",
      "             size    bedrooms   bathrooms         age  garage_size  \\\n",
      "count  100.000000  100.000000  100.000000  100.000000   100.000000   \n",
      "mean   116.884604    5.348844    3.772738   15.854721     2.362021   \n",
      "std     27.245053    0.673347    0.568828    7.072811     0.428735   \n",
      "min     41.407647    3.844226    2.326662   -1.991166     1.519419   \n",
      "25%    101.972830    4.945049    3.313872   10.463845     2.045270   \n",
      "50%    116.191311    5.341064    3.843428   15.401259     2.353606   \n",
      "75%    132.178562    5.727595    4.126039   20.472006     2.626775   \n",
      "max    175.568346    7.290621    5.199971   32.518423     3.645316   \n",
      "\n",
      "       near_school       price  \n",
      "count   100.000000  100.000000  \n",
      "mean      0.560000  314.193294  \n",
      "std       0.498888   39.978504  \n",
      "min       0.000000  230.415753  \n",
      "25%       0.000000  284.131490  \n",
      "50%       1.000000  311.499705  \n",
      "75%       1.000000  343.295700  \n",
      "max       1.000000  402.891795  \n",
      "\n",
      "缺失值检查:\n",
      "size           0\n",
      "bedrooms       0\n",
      "bathrooms      0\n",
      "age            0\n",
      "garage_size    0\n",
      "near_school    0\n",
      "price          0\n",
      "dtype: int64\n",
      "\n",
      "Age列最小值: -1.991165794478455\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:20:42.758208Z",
     "start_time": "2025-11-25T10:20:42.739719Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2. 数据清洗和特征工程\n",
    "# 处理异常值 - 将负的年龄转换为0\n",
    "data['age'] = data['age'].apply(lambda x: max(x, 0))\n",
    "\n",
    "# 准备特征和目标变量\n",
    "X = data.drop('price', axis=1)\n",
    "y = data['price']\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(f\"训练集大小: {X_train.shape}\")\n",
    "print(f\"测试集大小: {X_test.shape}\")"
   ],
   "id": "f013e2f3a7cc21ea",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: (80, 6)\n",
      "测试集大小: (20, 6)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:21:16.383768Z",
     "start_time": "2025-11-25T10:21:16.239442Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3. 模型训练和评估\n",
    "# 定义三个模型\n",
    "models = {\n",
    "    '线性回归': LinearRegression(),\n",
    "    '随机森林': RandomForestRegressor(n_estimators=100, random_state=42),\n",
    "    '支持向量机': SVR(kernel='rbf', C=1.0)\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 训练和评估每个模型\n",
    "for name, model in models.items():\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"训练 {name} 模型...\")\n",
    "\n",
    "    # 训练模型（对于线性模型使用标准化数据）\n",
    "    if name == '线性回归' or name == '支持向量机':\n",
    "        model.fit(X_train_scaled, y_train)\n",
    "        y_pred = model.predict(X_test_scaled)\n",
    "    else:\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "    # 计算评价指标\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "\n",
    "    # 存储结果\n",
    "    results[name] = {\n",
    "        'MSE': mse,\n",
    "        'MAE': mae,\n",
    "        'R2': r2,\n",
    "        'RMSE': rmse\n",
    "    }\n",
    "\n",
    "    print(f\"{name} 模型结果:\")\n",
    "    print(f\"均方误差 (MSE): {mse:.4f}\")\n",
    "    print(f\"平均绝对误差 (MAE): {mae:.4f}\")\n",
    "    print(f\"决定系数 (R²): {r2:.4f}\")\n",
    "    print(f\"均方根误差 (RMSE): {rmse:.4f}\")"
   ],
   "id": "5871ad3ab5ddb3b6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "训练 线性回归 模型...\n",
      "线性回归 模型结果:\n",
      "均方误差 (MSE): 28.3198\n",
      "平均绝对误差 (MAE): 4.1271\n",
      "决定系数 (R²): 0.9777\n",
      "均方根误差 (RMSE): 5.3216\n",
      "\n",
      "==================================================\n",
      "训练 随机森林 模型...\n",
      "随机森林 模型结果:\n",
      "均方误差 (MSE): 66.4785\n",
      "平均绝对误差 (MAE): 6.8710\n",
      "决定系数 (R²): 0.9477\n",
      "均方根误差 (RMSE): 8.1534\n",
      "\n",
      "==================================================\n",
      "训练 支持向量机 模型...\n",
      "支持向量机 模型结果:\n",
      "均方误差 (MSE): 952.2753\n",
      "平均绝对误差 (MAE): 26.0082\n",
      "决定系数 (R²): 0.2507\n",
      "均方根误差 (RMSE): 30.8590\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:51:32.469227Z",
     "start_time": "2025-11-25T10:51:31.937200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 4. 结果比较和可视化\n",
    "# 创建结果比较DataFrame\n",
    "results_df = pd.DataFrame(results).T\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"Model Performance Comparison:\")\n",
    "print(results_df)\n",
    "\n",
    "# 可视化比较\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# MSE比较\n",
    "axes[0,0].bar(results_df.index, results_df['MSE'], color=['skyblue', 'lightcoral', 'lightgreen'])\n",
    "axes[0,0].set_title('MSE Comparison\\n(Lower is Better)')\n",
    "axes[0,0].set_ylabel('MSE')\n",
    "# 添加数值标签\n",
    "for i, v in enumerate(results_df['MSE']):\n",
    "    axes[0,0].text(i, v, f'{v:.1f}', ha='center', va='bottom')\n",
    "\n",
    "# MAE比较\n",
    "axes[0,1].bar(results_df.index, results_df['MAE'], color=['skyblue', 'lightcoral', 'lightgreen'])\n",
    "axes[0,1].set_title('MAE Comparison\\n(Lower is Better)')\n",
    "axes[0,1].set_ylabel('MAE')\n",
    "# 添加数值标签\n",
    "for i, v in enumerate(results_df['MAE']):\n",
    "    axes[0,1].text(i, v, f'{v:.1f}', ha='center', va='bottom')\n",
    "\n",
    "# R²比较\n",
    "axes[1,0].bar(results_df.index, results_df['R2'], color=['skyblue', 'lightcoral', 'lightgreen'])\n",
    "axes[1,0].set_title('R-squared Comparison\\n(Higher is Better)')\n",
    "axes[1,0].set_ylabel('R2 Score')\n",
    "# 添加数值标签\n",
    "for i, v in enumerate(results_df['R2']):\n",
    "    axes[1,0].text(i, v, f'{v:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# RMSE比较\n",
    "axes[1,1].bar(results_df.index, results_df['RMSE'], color=['skyblue', 'lightcoral', 'lightgreen'])\n",
    "axes[1,1].set_title('RMSE Comparison\\n(Lower is Better)')\n",
    "axes[1,1].set_ylabel('RMSE')\n",
    "# 添加数值标签\n",
    "for i, v in enumerate(results_df['RMSE']):\n",
    "    axes[1,1].text(i, v, f'{v:.1f}', ha='center', va='bottom')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 预测值与真实值对比可视化\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "for idx, (name, model) in enumerate(models.items()):\n",
    "    if name == 'Linear Regression' or name == 'Support Vector Machine':\n",
    "        y_pred = model.predict(X_test_scaled)\n",
    "    else:\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "    axes[idx].scatter(y_test, y_pred, alpha=0.6)\n",
    "    axes[idx].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "    axes[idx].set_xlabel('Actual Price')\n",
    "    axes[idx].set_ylabel('Predicted Price')\n",
    "    axes[idx].set_title(f'{name} - Prediction vs Actual')\n",
    "\n",
    "    # 添加R²到图中\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    axes[idx].text(0.05, 0.95, f'R2 = {r2:.4f}', transform=axes[idx].transAxes,\n",
    "                  bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"white\", alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "d194d9e7bc1ec2b4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Model Performance Comparison:\n",
      "                               MSE        MAE        R2       RMSE\n",
      "Linear Regression        28.319784   4.127059  0.977717   5.321634\n",
      "Random Forest            66.478531   6.870989  0.947692   8.153437\n",
      "Support Vector Machine  952.275316  26.008187  0.250709  30.858958\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ],
      "image/png": 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AAAAAgBWE6ADwBKZNm6bGjRsrISHB3Obt7a2RI0cm2/+HH36Qt7e3fv31V3Pb1KlT5e3t/UTrX7Nmjby9vXX06NEnmt8WEmu4cOFCmpc1ZMgQeXt7m/+99NJLqlWrlvr27avTp08/0TLv3r2rqVOnWuzzRAcOHNDUqVN169attJaerA8++EDvvvtuuiwbAAAAD6R2TP60q1evnoYMGZLm5Vy4cMFi7O3t7a1KlSqpWbNmWrhwoeLj459ouTt27NDUqVOTnfbFF19oy5YtaSnbqr///ls+Pj46fvx4uiwfACRCdABItatXr2revHnq06eP7Oye/GO0devWWrFihQ0ry1h16tTRihUr5ObmZpPl5c6dWytWrNCKFSu0ePFivf/++/r999/Vrl07Xb16NdXLu3v3rqZNm6a9e/cmmXbw4EFNmzYt3UL03r17a8eOHfrll1/SZfkAAADPOluNyZ8m06ZNs+mJGh06dDCPvydNmqRKlSopJCRE48aNe6Ll7dixQ9OmTUt22qxZs9ItRC9VqpSaNm2qkJCQdFk+AEhSjswuAACeNosWLVL+/PnVsGHDNC3H3d1d7u7uNqoqfdy9e1d58uRJdlqhQoVUqFAhm63Lzs5OFSpUML9++eWX9fzzz+udd97R9u3b1bZtW5utK70k7q/ixYurZs2amjNnjgICAjK7LAAAgGzHVmPyrCQ+Pl7x8fFycHBIdnrZsmVtur7nn3/eYvxdq1YtnTlzRuvXr7fJGe/p7eH91b59e7Vq1UoHDhxQpUqVMrs0ANnQs/HnWgCwkZiYGK1atUpNmjRJ8xkvyd3OJSYmRmPGjFGNGjXk5+en9u3b69ixY1Yv3YyOjtYnn3wif39/+fv7q1evXsmetb1hwwa1bdtWFSpUUMWKFdW5c2f9/vvvFn2GDBmiihUr6tSpU+rUqZMqVqyod955x2r9yd3O5ffff1f37t0VEBAgHx8fBQYGqlu3brpy5Uoq984D+fPnlyTlyGH5N9+wsDB9/PHHqlWrlnx8fFSvXj1NmzZNcXFxkh5copoYXk+bNs18meqQIUM0depUff7555Kk+vXrm6c9fNsXW+yvZs2aaffu3Tp37twTbTsAAACSl9Yx+Y0bNzR8+HDVrFlTPj4+ql+/viZOnKiYmBhznz59+uj111+3mK9Hjx7y9vbWxo0bzW3Hjx+Xt7e3tm3bZm573FhV+t8tVebMmaMZM2aoXr168vX11Z49e6zW/e/vBAkJCZoxY4YaNWqk8uXL6+WXX1bTpk315ZdfpnqfJMqfP79y5syZpP1x4+MhQ4ZoyZIlkmRxm5jE7bxz547Wrl1rbu/QoYNN95ePj488PT21fPnyJ952AHgUzkQHgFQ4cuSIbty4IX9//2SnG4ZhMdhL9PB9Gh9l6NCh2rBhg7p06aJq1arpjz/+UK9evXT79u1k+3/00UeqU6eOQkNDdfnyZY0bN04ffPCBFi1aZO7zxRdfaNKkSQoKClLPnj0VGxurefPmqX379lq5cqVeeOEFc9/Y2Fj17NlT7dq1U9euXVN1P8Q7d+4oODhYRYsW1ccffywXFxeFhYXp119/VXR0dIqWkbjv4uPj9c8//+jzzz9XgQIFVKdOHXOfsLAwtW7dWnZ2dnrvvfdUvHhxHTx4UDNnztTFixcVEhIiNzc3zZ07V126dNEbb7yh1q1bS3pw9ryDg4Nu3rypxYsXa9q0aXJ1dZUk836w1f7y9/eXYRjasWOHxZcEAAAApM3jxuSPcv/+fXXs2FHnz59X79695e3trf3792v27Nk6ceKEZs+eLUmqXr26Nm3apGvXrsnNzU1xcXHau3evcufOrd27d+u1116TJO3evVs5cuRQ1apVJaVsrPqwxYsXq2TJkho8eLAcHR1VokSJFG/L3LlzNW3aNPXs2VMvv/yy4uLi9NdffykqKipF8yckJJjH31FRUdq6dat27typLl26WPRLyfj43Xff1Z07d7Rp0yaLW1a6ublpxYoVevvtt+Xv72++HY2jo6PN91fVqlX1ww8/yDAMmUymFO9HAEgJQnQASIWDBw9KksqVK5fs9KVLl2rp0qVPtOw//vhD69evV9euXTVgwABJUo0aNeTi4qL+/fsnO0/NmjX10UcfmV/fvHlT48aNU1hYmFxdXXX58mVNnTpVb731lkW/6tWrq1GjRpo2bZomTZpkbo+NjdV7772nVq1apbr+v/76Szdu3NBnn32mBg0amNsbN26covnv3LmTZL+6urpq5syZcnZ2NrdNnTpVN2/e1Pfff68iRYpIkgICApQ7d26NHTtWnTt31gsvvGBelru7u8VlqtKDS1cl6aWXXlLRokXN7bbcX87OzipcuLAOHDhAiA4AAGBDjxuTP8ratWt16tQpTZo0yRyE16hRQ3nz5tX48eO1a9cu1ahRQ9WrV5f0ICRv0aKFDh8+rOjoaHXp0kU//PCDeXm//PKLfH19zaFwSseqiXLlyqV58+Yle/b34xw4cEBeXl7q3bu3ua1mzZopnn/8+PEaP368RVtQUJD69Oljfp3S8XHx4sXl4uIiSUnG3hUqVJCdnZ0KFSqUZJot91e5cuW0bNky/fXXX/L09EzxfgCAlOB2LgCQCteuXZPJZJKTk1Oy01977TWtWrUqyb+BAwc+dtmJD8BMHMwnatSoUZLbmSSqV6+exevE28NcunRJkvTzzz8rLi5OzZs3V1xcnPlfrly5VKVKlWQfutmoUaPH1pqcEiVKqECBAho/fryWLVumP/74I1Xz586d27y/Vq5cqWnTpqlUqVLq1q2b+YuSJG3fvl3+/v7mM4IS/9WqVUuSkt2mlLL1/nJ2dn6ih6ICAADAuseNyR9lz549yps3r1599VWL9qCgIEkyPxi+ePHi8vDwML/evXu3vLy81KxZM124cEHnzp1TTEyMfvvtN3PgLqV+rFqvXr0nCtAlydfXVydPntTw4cO1c+dOq1evWtOxY0fz+HvRokXq37+/Nm7caHECz5OMj1PDlvsr8XlN165dS1NNAJAczkQHgFS4f/++cuTIIXt7+2SnFypUSL6+vknaL168+Nhl37hxQ5LMZ3AkypEjhwoWLJjsPP9uT3wI0b179yRJ4eHhkqQ33ngj2fn/fQ/JPHnymM+iSa38+fNr8eLF+uKLLzRx4kTdvHlTrq6uatOmjXr27PnYLwd2dnZJ9l1gYKDq1KmjMWPGmC8LjYiI0E8//WT1zKPIyMgnql+y/f7KlSuX+b0AAACAbTxuTP4oN27ckIuLS5LbfTg7OytHjhzmMbn04IzonTt3SnoQoteoUUPe3t5ycXHR7t27VaJECd27d88iRE/tWDXx1oJPonv37sqbN6++/fZbLV++XPb29nr55Zc1cODAZL+T/Ju7u7tFP39/f5lMJoWGhmrnzp2qWbNmqsfHqWXL/ZUrVy5JYvwNIF0QogNAKjg5OSk2NlZ37txR3rx5bbrsxEA8PDxchQsXNrfHxcVZDOZTI/HsnClTppgvj3yUtN470NvbWxMnTpRhGDp16pTWrFmj6dOnK3fu3OrWrVuql5cnTx4VK1ZMJ0+eNLc5OTnJ29tb77//frLzuLm5PWn5Nt9fN27ckIeHxxPXAwAAgKTSMiYvWLCgDh8+nOS+2REREYqLi7M4uz0gIECrVq3SkSNHdOTIEfXs2VOSVK1aNe3evVuXLl1S3rx55efnZ1FbasaqaRl/58iRQ8HBwQoODtatW7e0e/duTZw4UV26dNH27duVJ0+eVC8z8crWkydPqmbNmqkeH6eWLffXzZs3zcsEAFsjRAeAVChVqpQk6dy5cypTpoxNl12lShVJD558//CZGJs2bUr2YaUpERgYqBw5cujcuXNPfJuWJ2EymVSmTBl9+OGHWrt2rY4fP/5Ey4mOjta5c+cs7olep04d7dixQ8WLF1eBAgWszvvvs/KTm3b//n2Ldlvur7i4OF25ckW1a9dO03IAAABgKS1j8oCAAG3cuFFbtmzRK6+8Ym5ft26defrDfU0mkyZPniyTyWQerwcEBGjcuHG6ePGiqlSpYnHFZUrHqrb23HPP6dVXX9XVq1c1evRoXbx40eJe4il14sQJSTKPv1MzPn54/J07d+4k05Ibl9tyf50/f152dnbmnw8AsCVCdABIBX9/f0nS4cOHbR6iv/jii2rSpIkWLFgge3t7VatWTWfOnNGCBQuUP3/+JzpLpWjRourTp48mTZqk8+fPq1atWnruuecUHh6uo0ePKk+ePBYPDkqLn376SUuXLlWDBg1UrFgxGYahzZs369atW6pRo8Zj509ISNChQ4fM/7969aoWL16smzdvqlevXuZ+ffr00e7du9WuXTt16NBBpUqVUkxMjC5cuKD/+7//04gRI+Tu7i5HR0d5eHho69atCggIUIECBeTk5KSiRYvKy8tLkvTll1+qZcuWypEjh0qVKmXT/XXq1CndvXvX/DMDAAAA23jcmPzcuXMWD/9M9MILL6hFixZasmSJBg8erIsXL8rLy0u//fabZs2apdq1a1vcmsXZ2Vkvvviifv75Z/n7+5vP7K5evbpu3LihGzduaOjQoRbrSOlY1RZ69OihF198UT4+PipUqJAuXryoL7/8Uh4eHipRosRj5798+bJ5/H337l0dPHhQs2fPloeHhxo2bCgpdd8nEsfYc+bMUa1atWRnZydvb285ODjIy8tLe/fu1bZt2+Tq6qp8+fKpdOnSNt1fhw4d0ksvvZShf7wA8OwgRAeAVHj++ef18ssva+vWrWrbtq3Nlx8SEiJXV1etWrVKCxcu1EsvvaRJkyapS5cueu65555omd27d5enp6cWLVqk77//XjExMXJ1dZWPj4/efPNNm9VeokQJPffcc5o7d66uXbumnDlzqlSpUhozZoxatmz52Pnv3btnsU+dnZ3l6emp6dOnq0GDBuZ2Nzc3rVq1SjNmzNC8efN09epV5cuXTx4eHqpZs6bFfvrss8/0+eefq2fPnoq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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:23:39.600594Z",
     "start_time": "2025-11-25T10:23:39.593419Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 5. 最优模型选择\n",
    "# 找出每个指标的最优模型\n",
    "best_models = {\n",
    "    'MSE': results_df['MSE'].idxmin(),\n",
    "    'MAE': results_df['MAE'].idxmin(),\n",
    "    'R2': results_df['R2'].idxmax(),\n",
    "    'RMSE': results_df['RMSE'].idxmin()\n",
    "}\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"最优模型分析:\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "for metric, best_model in best_models.items():\n",
    "    best_value = results_df.loc[best_model, metric]\n",
    "    print(f\"{metric}: {best_model} ({best_value:.4f})\")\n",
    "\n",
    "# 确定最终最优模型\n",
    "# 统计每个模型在各项指标中排名第一的次数\n",
    "model_wins = {model: 0 for model in models.keys()}\n",
    "for metric in ['MSE', 'MAE', 'R2', 'RMSE']:\n",
    "    if metric == 'R2':  # R2是越大越好\n",
    "        best_model = results_df[metric].idxmax()\n",
    "    else:  # 其他指标都是越小越好\n",
    "        best_model = results_df[metric].idxmin()\n",
    "    model_wins[best_model] += 1\n",
    "\n",
    "print(\"\\n各模型在评价指标中排名第一的次数:\")\n",
    "for model, wins in model_wins.items():\n",
    "    print(f\"{model}: {wins}次\")\n",
    "\n",
    "# 选择最优模型\n",
    "max_wins = max(model_wins.values())\n",
    "best_models_final = [model for model, wins in model_wins.items() if wins == max_wins]\n",
    "\n",
    "print(f\"\\n最终最优模型: {best_models_final}\")\n",
    "\n",
    "if len(best_models_final) == 1:\n",
    "    print(f\"推荐使用 {best_models_final[0]} 模型进行房屋价格预测\")\n",
    "else:\n",
    "    print(\"多个模型表现相当，可以根据具体需求选择:\")\n",
    "    for model in best_models_final:\n",
    "        print(f\"  - {model}\")"
   ],
   "id": "97b9fd4c07dcd41",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "最优模型分析:\n",
      "============================================================\n",
      "MSE: 线性回归 (28.3198)\n",
      "MAE: 线性回归 (4.1271)\n",
      "R2: 线性回归 (0.9777)\n",
      "RMSE: 线性回归 (5.3216)\n",
      "\n",
      "各模型在评价指标中排名第一的次数:\n",
      "线性回归: 4次\n",
      "随机森林: 0次\n",
      "支持向量机: 0次\n",
      "\n",
      "最终最优模型: ['线性回归']\n",
      "推荐使用 线性回归 模型进行房屋价格预测\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:48:55.533282Z",
     "start_time": "2025-11-25T10:48:55.423266Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 6. 特征重要性分析（对于树模型）\n",
    "# 分析随机森林的特征重要性\n",
    "if 'Random Forest' in models:\n",
    "    rf_model = models['Random Forest']\n",
    "    feature_importance = pd.DataFrame({\n",
    "        'feature': X.columns,\n",
    "        'importance': rf_model.feature_importances_\n",
    "    }).sort_values('importance', ascending=False)\n",
    "\n",
    "    print(\"\\nRandom Forest Feature Importance:\")\n",
    "    print(feature_importance)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(data=feature_importance, x='importance', y='feature')\n",
    "    plt.title('Random Forest Feature Importance')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ],
   "id": "a71bfc9aca3d14bc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Feature Importance:\n",
      "       feature  importance\n",
      "0         size    0.788656\n",
      "2    bathrooms    0.090885\n",
      "1     bedrooms    0.051657\n",
      "5  near_school    0.033492\n",
      "4  garage_size    0.019457\n",
      "3          age    0.015854\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:55:21.407947Z",
     "start_time": "2025-11-25T10:55:21.389945Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 二、\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('interest_hobbies_dataset.csv')\n",
    "\n",
    "print(\"数据形状:\", data.shape)\n",
    "print(\"\\n数据前5行:\")\n",
    "print(data.head())\n",
    "print(\"\\n数据基本信息:\")\n",
    "print(data.info())\n",
    "print(\"\\n数据描述性统计:\")\n",
    "print(data.describe())\n",
    "print(\"\\n兴趣爱好分布:\")\n",
    "print(data['兴趣爱好'].value_counts())"
   ],
   "id": "feca4db5d7991d7c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (1000, 4)\n",
      "\n",
      "数据前5行:\n",
      "   年龄  性别          收入水平 兴趣爱好\n",
      "0  58   0   5498.046632   阅读\n",
      "1  48   1   6178.677457   阅读\n",
      "2  34   1  10953.849143   旅行\n",
      "3  27   0   6990.736311   旅行\n",
      "4  40   1  13513.112724   旅行\n",
      "\n",
      "数据基本信息:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   年龄      1000 non-null   int64  \n",
      " 1   性别      1000 non-null   int64  \n",
      " 2   收入水平    1000 non-null   float64\n",
      " 3   兴趣爱好    1000 non-null   object \n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 31.4+ KB\n",
      "None\n",
      "\n",
      "数据描述性统计:\n",
      "                年龄           性别          收入水平\n",
      "count  1000.000000  1000.000000   1000.000000\n",
      "mean     40.338000     0.485000  12563.624477\n",
      "std      11.980872     0.500025   4326.158583\n",
      "min      20.000000     0.000000   5048.273954\n",
      "25%      30.000000     0.000000   8707.986715\n",
      "50%      41.000000     0.000000  12760.365561\n",
      "75%      51.000000     1.000000  16223.922032\n",
      "max      60.000000     1.000000  19991.205887\n",
      "\n",
      "兴趣爱好分布:\n",
      "兴趣爱好\n",
      "旅行    423\n",
      "阅读    245\n",
      "运动    170\n",
      "音乐    162\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T11:02:20.579103Z",
     "start_time": "2025-11-25T11:02:20.099161Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据预处理\n",
    "# 编码性别和兴趣爱好\n",
    "data['性别'] = data['性别'].map({0: '女', 1: '男'})\n",
    "le_hobby = LabelEncoder()\n",
    "data['兴趣爱好_编码'] = le_hobby.fit_transform(data['兴趣爱好'])\n",
    "\n",
    "print(\"\\n编码后的兴趣爱好:\")\n",
    "for i, hobby in enumerate(le_hobby.classes_):\n",
    "    print(f\"{hobby}: {i}\")\n",
    "\n",
    "# 准备特征和目标变量\n",
    "X = data[['年龄', '性别', '收入水平']]\n",
    "y = data['兴趣爱好_编码']\n",
    "\n",
    "# 对性别进行one-hot编码\n",
    "X = pd.get_dummies(X, columns=['性别'], drop_first=True)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(f\"\\n训练集大小: {X_train.shape}\")\n",
    "print(f\"测试集大小: {X_test.shape}\")\n",
    "print(f\"特征名称: {X.columns.tolist()}\")\n",
    "\n",
    "# 检查数据分布\n",
    "print(\"\\n训练集类别分布:\")\n",
    "print(y_train.value_counts().sort_index())\n",
    "print(\"\\n测试集类别分布:\")\n",
    "print(y_test.value_counts().sort_index())\n",
    "\n",
    "# 定义分类模型\n",
    "models = {\n",
    "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "    'SVM': SVC(kernel='rbf', random_state=42),\n",
    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=1000)\n",
    "}\n",
    "\n",
    "# 训练和评估模型\n",
    "results = {}\n",
    "predictions = {}\n",
    "classification_reports = {}\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型训练和评估\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "for name, model in models.items():\n",
    "    print(f\"\\n训练 {name} 模型...\")\n",
    "\n",
    "    # 训练模型\n",
    "    if name in ['SVM', 'Logistic Regression']:\n",
    "        model.fit(X_train_scaled, y_train)\n",
    "        y_pred = model.predict(X_test_scaled)\n",
    "    else:\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "    # 计算准确率\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    results[name] = accuracy\n",
    "    predictions[name] = y_pred\n",
    "\n",
    "    # 显示预测分布\n",
    "    unique_pred, counts_pred = np.unique(y_pred, return_counts=True)\n",
    "    print(f\"预测类别分布: {dict(zip(unique_pred, counts_pred))}\")\n",
    "\n",
    "    # 使用zero_division=0避免警告\n",
    "    print(f\"\\n{name} 分类报告:\")\n",
    "    print(classification_report(y_test, y_pred, target_names=le_hobby.classes_, zero_division=0))\n",
    "\n",
    "    # 存储分类报告\n",
    "    report = classification_report(y_test, y_pred, target_names=le_hobby.classes_, output_dict=True, zero_division=0)\n",
    "    classification_reports[name] = report\n",
    "\n",
    "    print(f\"{name} 准确率: {accuracy:.4f}\")\n",
    "\n",
    "# 比较模型性能\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型性能比较\")\n",
    "print(\"=\"*60)\n",
    "results_df = pd.DataFrame(list(results.items()), columns=['Model', 'Accuracy'])\n",
    "results_df = results_df.sort_values('Accuracy', ascending=False)\n",
    "print(results_df)\n",
    "\n",
    "# 可视化准确率比较\n",
    "plt.figure(figsize=(10, 6))\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "bars = plt.bar(results_df['Model'], results_df['Accuracy'], color=colors)\n",
    "plt.title('Model Accuracy Comparison', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0, 1)\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# 添加数值标签\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "             f'{height:.4f}', ha='center', va='bottom', fontweight='bold', fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 选择最佳模型\n",
    "best_model_name = results_df.iloc[0]['Model']\n",
    "best_accuracy = results_df.iloc[0]['Accuracy']\n",
    "print(f\"\\n最佳模型: {best_model_name} (准确率: {best_accuracy:.4f})\")\n",
    "\n",
    "# 显示最佳模型的混淆矩阵\n",
    "best_model_predictions = predictions[best_model_name]\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, best_model_predictions)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "           xticklabels=le_hobby.classes_,\n",
    "           yticklabels=le_hobby.classes_)\n",
    "plt.title(f'{best_model_name} - Confusion Matrix', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 显示最佳模型的详细性能\n",
    "print(f\"\\n{best_model_name} 详细性能分析:\")\n",
    "best_report = classification_reports[best_model_name]\n",
    "for hobby in le_hobby.classes_:\n",
    "    precision = best_report[hobby]['precision']\n",
    "    recall = best_report[hobby]['recall']\n",
    "    f1 = best_report[hobby]['f1-score']\n",
    "    support = best_report[hobby]['support']\n",
    "    print(f\"  {hobby}: 精确率={precision:.3f}, 召回率={recall:.3f}, F1-score={f1:.3f}, 样本数={support}\")\n",
    "\n",
    "# 特征重要性分析（对于树模型）\n",
    "if best_model_name == 'Random Forest':\n",
    "    best_model = models[best_model_name]\n",
    "    feature_importance = pd.DataFrame({\n",
    "        'feature': X.columns,\n",
    "        'importance': best_model.feature_importances_\n",
    "    }).sort_values('importance', ascending=False)\n",
    "\n",
    "    print(\"\\n随机森林特征重要性:\")\n",
    "    print(feature_importance)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(data=feature_importance, x='importance', y='feature')\n",
    "    plt.title('Random Forest Feature Importance')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ],
   "id": "95da7226f635a0aa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "编码后的兴趣爱好:\n",
      "旅行: 0\n",
      "运动: 1\n",
      "阅读: 2\n",
      "音乐: 3\n",
      "\n",
      "训练集大小: (800, 2)\n",
      "测试集大小: (200, 2)\n",
      "特征名称: ['年龄', '收入水平']\n",
      "\n",
      "训练集类别分布:\n",
      "兴趣爱好_编码\n",
      "0    338\n",
      "1    136\n",
      "2    196\n",
      "3    130\n",
      "Name: count, dtype: int64\n",
      "\n",
      "测试集类别分布:\n",
      "兴趣爱好_编码\n",
      "0    85\n",
      "1    34\n",
      "2    49\n",
      "3    32\n",
      "Name: count, dtype: int64\n",
      "\n",
      "============================================================\n",
      "模型训练和评估\n",
      "============================================================\n",
      "\n",
      "训练 Random Forest 模型...\n",
      "预测类别分布: {np.int64(0): np.int64(80), np.int64(1): np.int64(39), np.int64(2): np.int64(49), np.int64(3): np.int64(32)}\n",
      "\n",
      "Random Forest 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          旅行       0.82      0.78      0.80        85\n",
      "          运动       0.51      0.59      0.55        34\n",
      "          阅读       1.00      1.00      1.00        49\n",
      "          音乐       1.00      1.00      1.00        32\n",
      "\n",
      "    accuracy                           0.83       200\n",
      "   macro avg       0.83      0.84      0.84       200\n",
      "weighted avg       0.84      0.83      0.84       200\n",
      "\n",
      "Random Forest 准确率: 0.8350\n",
      "\n",
      "训练 SVM 模型...\n",
      "预测类别分布: {np.int64(0): np.int64(115), np.int64(2): np.int64(51), np.int64(3): np.int64(34)}\n",
      "\n",
      "SVM 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          旅行       0.70      0.95      0.81        85\n",
      "          运动       0.00      0.00      0.00        34\n",
      "          阅读       0.94      0.98      0.96        49\n",
      "          音乐       0.94      1.00      0.97        32\n",
      "\n",
      "    accuracy                           0.81       200\n",
      "   macro avg       0.65      0.73      0.68       200\n",
      "weighted avg       0.68      0.81      0.73       200\n",
      "\n",
      "SVM 准确率: 0.8050\n",
      "\n",
      "训练 Logistic Regression 模型...\n",
      "预测类别分布: {np.int64(0): np.int64(77), np.int64(1): np.int64(29), np.int64(2): np.int64(54), np.int64(3): np.int64(40)}\n",
      "\n",
      "Logistic Regression 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          旅行       0.62      0.56      0.59        85\n",
      "          运动       0.45      0.38      0.41        34\n",
      "          阅读       0.76      0.84      0.80        49\n",
      "          音乐       0.72      0.91      0.81        32\n",
      "\n",
      "    accuracy                           0.66       200\n",
      "   macro avg       0.64      0.67      0.65       200\n",
      "weighted avg       0.64      0.66      0.65       200\n",
      "\n",
      "Logistic Regression 准确率: 0.6550\n",
      "\n",
      "============================================================\n",
      "模型性能比较\n",
      "============================================================\n",
      "                 Model  Accuracy\n",
      "0        Random Forest     0.835\n",
      "1                  SVM     0.805\n",
      "2  Logistic Regression     0.655\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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ttqenp6pUqaLt27fLZrOpdOnS6t27tz7++GM988wzeuyxx3TixAnNnTtXVatWVaNGjSRJTzzxhMLDwzVx4kQdO3ZMlStX1vbt27Vy5Ur17NnT3lMZFBSkr7/+WpMnT9bTTz+tDRs2aPr06SpVqlSmt+Hhhx+Wg4ODPvnkE9lsNp08eVLz589XbGys/UZxktSjRw8tX75cgwcP1h9//KEyZcpo7dq12r17t4YPH37DGQUdO3ZURESEHBwcFBoamum6pOtfMIwePVqDBg1S165d1blzZ/3555/6+uuvVaNGDdWpU0eS1L9/f61bt069evVSjx49VKBAAX355ZeSdMte6Fv55Zdf9PLLL6tGjRr68ccfFRkZqUGDBtlv9Obj46O1a9fq9ddfV9WqVfXzzz/rxx9/lKQ0X/xkJRcXF1WoUEGTJ0/WuXPn5OXlpaNHj2rdunUKDAy0f7lh4v0AAJhHjzcA4I5VqFBBBQoUuGnwlq7f2OvVV1/Vnj17NG7cOB0/ftwetlI9+uij+uyzz1SwYEFNnDhRixcv1siRI2+45rhJkyb6/PPP5e7ursmTJ2v27NkKDg7Wl19+aaSXL/U089Se6n+qX7++EhIStHnzZknSK6+8orfeekvR0dF6++23tWrVKnXr1k2zZ8+2X2vs6Oiozz77TM8995zWrVuncePG6eDBgxo7dqxee+01+7JbtGih559/XhEREfae5c8///yOgneFChX07rvvKikpSWPGjNHy5cv1zDPP6Nlnn9X58+e1a9cuSdd7sefOnav27dsrIiJCEyZM0IULF/TRRx/d8OxwSapevbo8PT0VHBx8wyPkMuPxxx/XjBkz5OrqqvHjx+uHH35Q165d9emnn9qvRy5WrJi+++47NWzYUF9++aWmTJmihx56SN988439BmqZ9fbbb+vcuXN655139Ndff2no0KHq3bu3fXrfvn3VqVMn/fTTT3r77bd1/vx5zZo1SyVKlNCGDRvu6mZ4tzJx4kSFhoZq2bJlGj16tCIiItSmTZs0j98z8X4AAMyzpKQ+YBQAAOAW/vzzT23atEnnz5/Xxx9/rI8//tj+uK3cIDw8XEOHDlVERIQqVaqU0+UAAO4jnGoOAAAyJDY2VpMmTZLValWfPn1yVegGACAn0eMNAAAAAIBBXOMNAAAAAIBBORa8L1y4oJCQEJ04cSJD8//yyy9q0aKFatasqVmzZhmuDgAAAACArJEjwTsmJkZ9+vTRyZMnMzz/Cy+8oJYtW2r+/PlaunSptmzZYrhKAAAAAADuXo4E74EDB+rxxx/P8PxLlixR0aJF9eKLL6pcuXLq27evFixYYLBCAAAAAACyRo4E7zFjxqh79+4Znn///v2qVauW/ZmeVatW1d69e02VBwAAAABAlsmR4F26dOlMzW+z2eTl5WUfdnNzU1RUVFaXBQAAAABAlssVz/G2Wq1ydna2D+fLl09xcXGZXs7581fEw9OQUywWqXDhgnwOAdzT2FcByA3YV+FekPo5zIhcEbw9PDwUExNjH46NjZWTk1Oml5OSIn4xkeP4HALIDdhXAcgN2Fcht8gVz/H28/PTzp077cP79u1TsWLFcrAiAAAAAAAy5p4K3jabTQkJCTeMDwkJ0Y4dO7RlyxYlJibq888/V3BwcA5UCAAAAABA5txTwbt169Zat27dDeM9PT312muv6bnnnlNwcLAOHjyoF154IQcqBAAAAAAgcywpKbnnqoi//vpLhw8fVo0aNeTm5pbp9ufOcfMF5ByLRSpSpCCfQwD3NPZVAHID9lW4F6R+DjMiV9xcLVXZsmVVtmzZnC4DAAAAAIAMu6dONQcAAAAAIK8heAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbyBbHb27Fnt2rVTV6/G5nQpAAAAALIBwRu4hT//PKTnnntazZs30kcffaCUlJTbtvn66y/VqlVTNW3aQK+//qouXbpon/b5558rLOxJvffeOLVr97h++22HfdrgwQMUHFzd/nr55b72ab/9tkNdu3ZQy5aNNW/enCzdRgAAAABmEbyBdMTHx+u11wbKx6eiZs78SkeP/qkfflh6yza///6rli9fpo8+mq7PP5+j+Ph4TZ36viTp2LG/NHPmTM2Z851mz56nLl3CNHPmp/a2+/fv05dfztPy5Wu0fPkavfPOREnShQsXNGTIQDVp0kyffDJLK1f+n379dbux7QYAAACQtQjeQDq2bNksm82m/v0HqlQpL/Xu/aKWLVt8yzZ79+5R7dp1VaZMOXl5lVaTJs10/PgxSVJSUpLGjBmjIkWKSJIeeuhhXblyWZIUHR0lKUXe3g+pYMGCKliwoFxcXCRJq1YtV+HCRdSjx3MqXbqMevZ87rZ1AAAAALh3ELyBdBw6dEC+vlWUP39+SdeD8tGjR27Zxtu7gtatW6OTJ0/owoUYLVu2WEFBNSVJ5ct7KyQkRJJ09epVLVgwX/XrN5Ik7du3R0lJyWrX7nE1aRKskSOH6vLly/+t46ACA4NksVgkSZUqVdGBA5FGthkAAABA1iN4A+mIjY1ViRKl7MMWi0VWq4M9EN9MrVp15OVVRp06tVWrVk0VF/e3unXrnmaezZs3qk2b5oqJOa/u3Z+VJB07dkw+PpU0ceIUzZjxlU6fPq3p0z/6bx02lSxZ0t7e1dVVZ89GZ+WmAgAAADCI4A2kw2q1ytnZKc04Z+d8unYtLt02q1evUlTUaX399QL98MNqlS9fQW+++UaaeWrUqKX33vtQVqtV06Z9IEkKC+uhiRM/lLd3BZUrV14vvNBfa9eu/m8djnJycv5HDc6Ki0u/BgAAAAD3FoI3kA53d3ddvHghzbirV2Pl6OiUTgvpxx9XqG3bUJUpU07u7h56+eVBWrduja5cuWKfx9HRUf7+1fTKK6+me7M2N7eCunjxouLj42+o4+rVq3JySr8GAAAAAPcWgjeQjkqVfLVnz2778OnTpxQfnyB3d/d02yQlJSkmJsY+fP78OUlScnKSVq78P33++ef2aVarVQ4OVknS8OGDtXv3Lvu0yMi9Kly4sJydnVWxYmXt2fO/aQcP7leRIg/e/QYCAAAAyBYEbyAd/v4BstlsWr58mSRpzpwvVL16DVmtVsXG2pSYmHhDGz8/fy1ZskgREQu0fPkyjRw5TFWqVJWHxwMqW7acpkyZonXr1uj06VOaOfNTNWrUWJJUocLDmjJlkvbs2a1NmzZoxoyP1a5dR0lScHAD/fHHTv3663YlJiZq3rw5qlmzVva9EQAAAADuiiUlJSUlp4vILufOXdH9s7XICuvXr9Xo0a+rQAFXJScnacqU6fL2rqAOHVrppZcGqX79hmnmv3btmj7++EOtXfuTLl26KF9fPw0dOkKlSnnJYpE2bfpJkye/rytXrqhRo8Z6+eX/KH/+/EpMTNS7776tNWtWq1ChQmrevKXCwnrK0dFRkhQe/p0+/HCi3NzclD+/i6ZP/0KenoVz4B0BkNdZLFKRIgX5mwngnsa+CveC1M9hhuYleAO3dvZstCIj96pKFX8VKlTojpdzt38gTpw4rqNHjyggIFCurm53XAcA3AoHswByA/ZVuBdkJng7Gq4FyPWKFn1QRYvm/DXVXl6l5eVVOqfLAAAAAJBJXOMNAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDXeN9jHBwscnCw5HQZMMhq5fuuvCg5OUXJydzdBTnnzz8P6e2339SJE8fVqlVb9e37kiyWW/89+frrL/XNN3N07do1BQXV0ODBr+uBBx6QJP322w69++44Xbx4QWFhPdW5czd7u8GDB2jz5g324UcfraEPPphmb/feezdvBwDA/YrgfQ9xcLDI44ECciSY5WmFCrnmdAkwIDEpWZcuXiV8I0fEx8frtdcGqkaNWho9+m29//67+uGHpWrZsnW6bX7//VctX75MH300XQ4OVn3wwXuaOvV9DR8+SjExMXrttYHq3LmbmjRpppEjh+mRRyoqMLC6JGn//n368st5Klq0mCTZH3144cIFDRmSfjsAAO5XBO97iIODRY5WB7087zcdirbldDkAMuihB930QecAOThYCN7IEVu2bJbNZlP//gOVP39+9e79oiZNGn/L4L137x7Vrl1XZcqUkyQ1adJMixYtkCQtWbJEhQsXUY8ez8lisahnz+e0bNliBQZWV3R0lKQUeXs/dMMyV61anm47AADuZwTve9ChaJv2nLqc02UAAHKJQ4cOyNe3ivLnzy9Jeuihh3X06JFbtvH2rqDJkxeqTZtQFShQQMuWLVZQUE1J0v79+/Xoo0H2U9UrVaqiTz/9SJK0b98eJSUlq127x3XlymXVrVtPgwYNlbu7uw4dOqjAwJu3AwDgfsY5zQAA5HKxsbEqUaKUfdhischqddDly+l/iVurVh15eZVRp05t1apVU8XF/a1u3bpLkmw2m0qUKGmf19XVVWfPRkuSjh07Jh+fSpo4cYpmzPhKp0+f1vTpH/23DptKlrx5OwAA7mcEbwAAcjmr1SpnZ6c045yd8+natbh026xevUpRUaf19dcL9MMPq1W+fAW9+eYb/1ie8z+W5ay4uOvLCgvroYkTP5S3dwWVK1deL7zQX2vXrv5vO0c5Od28HQAA9zOCNwAAuZy7u7suXryQZtzVq7FydHRKp4X0448r1LZtqMqUKSd3dw+9/PIgrVu3RleuXJGHh4cuXLjwj2VdlZPTzZfl5lZQFy9eVHx8/A113KodAAD3E4I3AAC5XKVKvtqzZ7d9+PTpU4qPT5C7u3u6bZKSkhQTE2MfPn/+nCQpOTlJfn5+2rNnl33awYP7VaTIg5Kk4cMHa/fu/02LjNyrwoULy9nZWRUrVk63HQAA9zOCNwAAuZy/f4BsNpuWL18mSZoz5wtVr15DVqtVsbE2JSYm3tDGz89fS5YsUkTEAi1fvkwjRw5TlSpV5eHxgEJCQvTHHzv166/blZiYqHnz5qhmzVqSpAoVHtaUKZO0Z89ubdq0QTNmfKx27TpKkoKDG6TbDgCA+xl3NQcAIJdzdHTU4MGva/To1zVt2odKTk7SlCnTJUndu3fRSy8NUv36DdO0efLJLjp//qy++GKmLl26KF9fPw0dOkKS5Onpqf79X9HAgf3k5uam/PldNGTI9eu/w8J66syZ0xow4EUVKlRIbdt2ULduPSRJDzzwgPr1u3k7AADuZ5aUlJT75qGz585d0b28tY6ODipUyFUtP9zA48SAXMS3pLu+f6meLlyIVWJick6Xg/vY2bPRiozcqypV/FWoUKE7WobFIhUpUlDnzl3R8ePHdfToEQUEBMrV1S3Dyzhx4s7aAUBG/XNfdS8f3yNvS/0cZgQ93gAA5BFFiz6ookWz7ppqL6/S8vIqnW3tAADIq7jGGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMCgHAneBw4cUGhoqIKCgjR+/Hhl5P5uM2bMUJ06dRQYGKj+/fvrwoUL2VApAAAAAAB3J9tvrhYfH68+ffooODhYkydP1tixYxUeHq7Q0NB022zbtk0RERGaM2eOrFar3nrrLY0fP17vvPNONlYOAEjl4GCRg4Mlp8uAQVYrJ8XlVcnJKUpO5jbQAJCdsj14r1+/XjabTUOHDpWLi4sGDhyo0aNH3zJ4//HHH6pfv768vb0lSS1bttQ333yTXSUDAP7BwcEizwfyy2LlwRh5WaFCrjldAgxJSUpUzMU4wjcAZKNsP2qKjIyUv7+/XFxcJEk+Pj46fPjwLds8/PDDmjdvnjp37ixXV1ctWLBAderUyY5yAQD/4uBguR66Fz4nnTuQ0+UAyIwij8gSOkMODhaCNwBko2wP3jabTV5eXvZhi8UiBwcHXbp0SR4eHjdtU79+fZUtW1aPPfaYJMnPz0+9e/fOlnoBAOk4d0A6vTOnqwAAALjnZXvwtlqtcnZ2TjMuX758iouLSzd4//DDDzp16pSWL18uT09PjR8/Xq+++qqmTJmSqXU7OVnvuO7swPWSQO7m6OhwX/we3w/bCOR198v+CnmX5b8fX2dnqzJwn2Ygx2V78Pbw8NDBgwfTjIuNjZWTk1O6bb7//nt16dLFfo3366+/rkcffVSXL1+Wu7t7htedkJB0T/9iOjpyIxsgN0tMTFZiYnJOl2Ec+yog97tf9lfIu1KDd3z8vX18j7zNkonvL7P96MnPz087d/7v1MQTJ04oPj4+3d5uSUpKStK5c+fsw9HR0fbxAAAAAADcy7K9xzsoKEhXrlxRRESE2rZtq+nTp6tOnTqyWq2y2WzKly/fDb3fgYGBmjVrlooXL678+fNr9uzZCggIUKFChbK7fAAAAAAAMiXbg7ejo6PGjBmjQYMGacKECUpKStKcOXMkSa1bt9awYcPUpEmTNG26d++u6OhoTZs2TRcuXFBAQIDeeuut7C4dAAAAAIBMy5GHsDZp0kQrV67Url27FBgYKE9PT0nSTz/9dNP58+XLp+HDh2v48OHZWSYAAAAAAHctR4K3JBUrVkzFihXLqdUDAAAAAJAtuDUtAAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAMgWf/55SM8997SaN2+kjz76QCkpKRluO3LkUE2ePCHNuMGDByg4uLr99fLLfTM07bffdqhr1w5q2bKx5s2bc/cbBtyGY04XAAAAACDvi4+P12uvDVSNGrU0evTbev/9d/XDD0vVsmXr27bduvVn7dixXd98E55mfGTkPn355TwVLVpMkuTo+L94s3//zadduHBBQ4YMVOfO3dSkSTONHDlMjzxSUYGB1bNqU4Eb0OMNAAAAwLgtWzbLZrOpf/+BKlXKS717v6hlyxbftt21a3GaOPEd9enTTwULFrSPP3PmjKQUeXs/pIIFC6pgwYJycXGRJEVHR6U7bdWq5SpcuIh69HhOpUuXUc+ez2WoDuBuELwBAAAAGHfo0AH5+lZR/vz5JUkPPfSwjh49ctt2X3wxU9euXZPVatWOHdvsp6f/8ccfSkpKVrt2j6tJk2CNHDlUly9fliTt27cn3WmHDh1UYGCQLBaLJKlSpSo6cCDSxCYDdgRvAAAAAMbFxsaqRIlS9mGLxSKr1cEeiG/mzJkzmj9/rry8Sisq6ow++ugDDRv2qlJSUnT06FFVrFhJEydO0YwZX+n06dOaPv0jSdKxY8fk43PzabGxNpUsWdK+DldXV509G21oq4HrCN4AAAAAjLNarXJ2dkozztk5n65di0u3zfLlS1WokKcmT/5IPXo8p6lTP9Xvv/+qX37Zqt69e2vixA/l7V1B5cqV1wsv9NfataslSWFhPdKdZrU6ysnJ+R81OCsuLv0agKxA8AYAAABgnLu7uy5evJBm3NWrsXJ0dEqnhRQdHa1HHw2Ss/P1oFyggKu8vErr9OmTN8zr5lZQFy9eVHx8/C2n/buOq1evyskp/RqArEDwBgAAAGBcpUq+2rNnt3349OlTio9PkLu7e7ptihUrpmvXrtmHk5OTdfZstIoXL6GXXnpJu3fvsk+LjNyrwoULy9nZWcOHD053WsWKlbVnz/+mHTy4X0WKPJhVmwncFMEbAAAAgHH+/gGy2WxavnyZJGnOnC9UvXoNWa1WxcbalJiYeEObkJAm2rx5g9auXa3o6Ch98slUJSTEy8/PX4888og+/HCS9uzZrU2bNmjGjI/Vrl1HSVKFCg9rypSbTwsObqA//tipX3/drsTERM2bN0c1a9bKvjcC9yWe4w0AAADAOEdHRw0e/LpGj35d06Z9qOTkJE2ZMl2S1L17F7300iDVr98wTZsyZcpp9Ohx+uyzafrrr79UqlQpjRs3Ua6urnr++ed15MhfGjDgRRUqVEht23ZQt249JElhYT115szpm0574IEH1K/fKxo4sJ/c3NyUP7+Lhgx5IxvfCdyPLCmp9+O/D5w7d0X38tY6OjqoUCFXtfxwg/acSv/ujgDuLb4l3fX9S/V04UKsEhOTc7oc41L3Vfq0vnR6Z06XAyAzSvhLz6+/b/ZXuDedPRutyMi9qlLFX4UKFbqjZVgsUpEiBe/q+P7EieM6evSIAgIC5erqdmcLwX0t9XOYEfR4AwAAAMg2RYs+qKJFc/6aai+v0vLyKp3TZeA+wTXeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIibqwEAACDPcnCwyMHBktNlwBCrlX7EvCo5OUXJyffwI6kyieANAACAPMnBwaIHCrnI6mDN6VJgSKFCrjldAgxJSk7SxQt/55nwTfAGAABAnuTgYJHVwaoh64foz0t/5nQ5ADLI28Nb79R/Rw4OFoI3AAAAkBv8eelP7YvZl9NlALiPcVEEAAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAINyJHgfOHBAoaGhCgoK0vjx45WSkpLhtgMGDNCYMWMMVgcAAAAAQNbJ9uAdHx+vPn36yNfXVwsXLtThw4cVHh6eobYbNmzQli1b9PLLLxuuEgAAAACArJHtwXv9+vWy2WwaOnSoypQpo4EDB2rBggW3bRcXF6fRo0dr0KBBcnd3z4ZKAQAAAAC4e9kevCMjI+Xv7y8XFxdJko+Pjw4fPnzbdtOmTVNcXJwcHR31888/Z+r0dAAAAAAAcopjdq/QZrPJy8vLPmyxWOTg4KBLly7Jw8Pjpm1OnTqlWbNmqWrVqjp16pS+/PJLlShRQlOnTpXFYsnwup2crHddv0kODhnfFgD3HkdHh/vi9/h+2EYgr2N/BSA3yEv7qmwP3larVc7OzmnG5cuXT3FxcekG7/DwcBUpUkSzZs2Ss7Oznn76aYWEhGjTpk0KDg7O8LoTEpJ0L3eUOzpyk3kgN0tMTFZiYnJOl2Ec+yog92N/BSA3uNf3VZnoA87+U809PDwUExOTZlxsbKycnJzSbRMVFaVatWrZA7ubm5vKli2rEydOGK0VAAAAAIC7le3B28/PTzt37rQPnzhxQvHx8en2dktS8eLFde3aNftwcnKyzpw5o5IlSxqtFQAAAACAu5XtwTsoKEhXrlxRRESEJGn69OmqU6eOrFarbDabEhISbmjTokULrVmzRitWrNCZM2c0ceJExcfHKzAwMJurBwAAAAAgc7I9eDs6OmrMmDEaOXKk6tSpoxUrVmjQoEGSpNatW2vdunU3tPH29tbkyZP18ccfq2nTplq3bp2mTZsmNze37C4fAAAAAIBMyfabq0lSkyZNtHLlSu3atUuBgYHy9PSUJP3000/ptmnYsKEaNmyYTRUCAAAAAJA1ciR4S1KxYsVUrFixnFo9AAAAAADZgmcsAAAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGJTp4H3kyBETdQAAAAAAkCc5ZrZBmzZtVKFCBbVo0UItWrRQ6dKlTdQFAAAAAECekOke7y1btuj555/XgQMHFBoaqo4dO+qLL77QmTNnTNQHAAAAAECulungXaBAATVv3lzvvfeeNm/erI4dO+qjjz5SSEiIunbtql9//dVEnQAAAAAA5EqZPtVcko4ePaqVK1dq5cqVOnTokOrXr68WLVooLi5OL730kjZu3JjVdQIAAAAAkCtlOni3atVKx44dU3BwsHr06KGQkBAVKFBAknT8+HF5enpmeZEAAAAAAORWmQ7ezz33nBo3biw3N7cbppUuXVpLlizJksIAAAAAAMgLMn2Nd5s2bdKE7vPnz2dpQQAAAAAA5CWZDt6HDh1Su3bttHz5cklSjx491LJlS57vDQAAAADATWQ6eI8YMUK1atVScHCwJGn+/Plq1KiRRo4cmeXFAQAAAACQ22U6eO/bt089e/ZUwYIFJV1/vFhYWJj27NmT5cUBAAAAAJDbZTp4+/j4aPHixWnGLV68WA8//HCWFQUAAAAAQF6R6buajxgxQr169VJERIRKlSqlEydO6PLly5oxY4aJ+gAAAAAAyNUyHbwrV66sFStWaM2aNYqKilKbNm3UoEGDmz5eDAAAAACA+12mg7ckubm5qVWrVmnGxcTEyNPTM0uKAgAAAAAgr8h08D506JAmTJigo0ePKikpSZKUkpKi6Oho7d69O8sLBAAAAAAgN8v0zdWGDh2qsmXLqnr16vL19dWIESOUL18+/ec//zFRHwAAAAAAuVqmg/fBgwfVp08fderUSadOnVKDBg00duxYhYeHm6gPAAAAAIBcLdPBu1y5clq4cKEqVqyo48ePKyYmRoULF9aJEydM1AcAAAAAQK6W6eA9fPhwzZ49WzabTaGhoWrSpIlCQ0PVuHFjE/UBAAAAAJCrZfrmatWrV9fGjRslSYMHD1bDhg119epV1atXL8uLAwAAAAAgt7ujx4lZLBb7zzVq1MiyYgAAAAAAyGsyfar53LlzFRUVZaIWAAAAAADynEwH76+++kr79+83UQsAAAAAAHlOpoN337599fHHHys2NtZEPQAAAAAA5CmZvsb72LFjSklJUbNmzdSmTRu5uLjYp/Xr1y9LiwMAAAAAILfLdPA+efKkypcvr/LlyysmJsZETQAAAAAA5BmZDt7jxo0zUQcAAAAAAHlSpoP3qVOn0p1WsmTJuyoGAAAAAIC8JtPBOyQkRBaLRSkpKZLSPtN73759WVcZAAAAAAB5QKaDd2RkpP3nuLg47dq1S1OnTlXfvn2ztDAAAAAAAPKCTD9O7J/y58+voKAgffzxxxo/fnxW1QQAAAAAQJ5xV8E71fnz5xUdHZ0ViwIAAAAAIE+542u8UyUnJ+vs2bN6+umns7QwAAAAAADygkwH73feeSfNsMViUfHixVW6dOksKwoAAAAAgLwi08G7Ro0aaYbPnz+vwoULZ1lBAAAAAADkJZm+xvvQoUNq166dli9fLknq0aOHWrZsqSNHjmR5cQAAAAAA5HaZDt4jRoxQrVq1FBwcLEmaP3++GjVqpJEjR2Z5cQAAAAAA5HaZDt779u1Tz549VbBgQUlSgQIFFBYWpj179mR5cQAAAAAA5HaZDt4+Pj5avHhxmnGLFy/Www8/nGVFAQAAAACQV2T65mojRoxQr169FBERoVKlSunEiRO6fPmyZsyYYaI+AAAAAABytUwH78qVK2vFihVau3atzpw5ozZt2qhBgwZyc3MzUR8AAAAAALlapoO3JLm5uemJJ56QdP1xYoRuAAAAAABujseJAQAAAABgEI8TAwAAAADAIB4nBgAAAACAQTxODAAAAAAAg+76cWLHjx/XlStXeJwYAAAAAAA3cdePE3v88cdVsGBBff/996pYsaKJGgEAAAAAyLXu6HFip06d0tmzZ7Vlyxbt2LFDiYmJqlatWhaXBgAAAABA7peh4H327Flt2rRJmzdv1ubNm3Xp0iVVrlxZe/fu1dixY/XYY4+pQIECpmsFAAAAACDXyVDwrlevniwWi2rUqKG33npLtWvXlrOzs4KCghQUFEToBgAAAAAgHRkK3rNmzbL3ePft21flypWTv7+/4uPjdf78eZUsWdJ0nQAAAAAA5EoZCt61a9dW7dq1JUkxMTHavHmzNm3apAceeEBPPvmkypQpo9q1a2vUqFEmawUAAAAAINfJ9M3VPD099cQTT+iJJ56QJB0+fFgbNmzQzz//nOXFAQAAAACQ293RXc3/qUKFCqpQoYJ69OiRBeUAAAAAAJC3OOR0AQAAAAAA5GUEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMypHgfeDAAYWGhiooKEjjx49XSkpKhtsmJCSoVatW2rp1q8EKAQAAAADIGtkevOPj49WnTx/5+vpq4cKFOnz4sMLDwzPcfsaMGTpw4IDBCgEAAAAAyDrZHrzXr18vm82moUOHqkyZMho4cKAWLFiQobZHjx7V559/rlKlShmuEgAAAACArJHtwTsyMlL+/v5ycXGRJPn4+Ojw4cMZajtixAj16tWL4A0AAAAAyDWyPXjbbDZ5eXnZhy0WixwcHHTp0qVbtlu4cKFsNpueeeYZ0yUCAAAAAJBlHLN7hVarVc7OzmnG5cuXT3FxcfLw8Lhpm5iYGE2aNEkzZsyQo+Odl+zkZL3jttnBwcGS0yUAuAuOjg73xe/x/bCNQF7H/gpAbpCX9lXZHrw9PDx08ODBNONiY2Pl5OSUbpu33npLHTp0UKVKle5q3QkJScrEDdSznaMjT3cDcrPExGQlJibndBnGsa8Ccj/2VwByg3t9X2XJxHcC2b438vPz086dO+3DJ06cUHx8fLq93ZK0bNkyffXVV6pevbqqV6+uHTt2qE+fPpo+fXp2lAwAAAAAwB3L9h7voKAgXblyRREREWrbtq2mT5+uOnXqyGq1ymazKV++fDf0fq9evTrN8MCBA9W9e3fVq1cvO0sHAAAAACDTsr3H29HRUWPGjNHIkSNVp04drVixQoMGDZIktW7dWuvWrbuhjZeXV5pXvnz5VKRIEbm7u2d3+QAAAAAAZEq293hLUpMmTbRy5Urt2rVLgYGB8vT0lCT99NNPGWr/1VdfmSwPAAAAAIAskyPBW5KKFSumYsWK5dTqAQAAAADIFtzqEQAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwKEeC94EDBxQaGqqgoCCNHz9eKSkpt20zf/58BQcHy9fXV88884yio6OzoVIAAAAAAO5Otgfv+Ph49enTR76+vlq4cKEOHz6s8PDwW7bZvn27PvjgA02YMEGrV6/WtWvXNH78+GyqGAAAAACAO5ftwXv9+vWy2WwaOnSoypQpo4EDB2rBggW3bHPkyBGNGjVKderUUfHixdW+fXvt3r07myoGAAAAAODOOWb3CiMjI+Xv7y8XFxdJko+Pjw4fPnzLNh07dkwzfOTIEZUtW9ZYjQAAAAAAZJVs7/G22Wzy8vKyD1ssFjk4OOjSpUsZan/hwgXNnz9fTz31lKkSAQAAAADIMtne4221WuXs7JxmXL58+RQXFycPD4/bth89erQCAgLUsGHDTK/bycma6TbZycHBktMlALgLjo4O98Xv8f2wjUBex/4KQG6Ql/ZV2R68PTw8dPDgwTTjYmNj5eTkdNu2CxYs0Pbt2xUREXFH605ISFIGbqCeYxwdebobkJslJiYrMTE5p8swjn0VkPuxvwKQG9zr+ypLJr4TyPa9kZ+fn3bu3GkfPnHihOLj42/b2/3HH3/o7bff1qRJk1SkSBHTZQIAAAAAkCWyPXgHBQXpypUr9l7r6dOnq06dOrJarbLZbEpISLihzblz59SnTx/16tVLvr6+io2NVWxsbDZXDgAAAABA5mV78HZ0dNSYMWM0cuRI1alTRytWrNCgQYMkSa1bt9a6detuaLNs2TKdP39e77//vgIDA+0vAAAAAADuddl+jbckNWnSRCtXrtSuXbsUGBgoT09PSdJPP/100/l79OihHj16ZGOFAAAAAABkjRwJ3pJUrFgxFStWLKdWDwAAAABAtuBWjwAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADCJ4AwAAAABgEMEbAAAAAACDCN4AAAAAABhE8AYAAAAAwCCCNwAAAAAABhG8AQAAAAAwiOANAAAAAIBBBG8AAAAAAAwieAMAAAAAYBDBGwAAAAAAgwjeAAAAAAAYRPAGAAAAAMAggjcAAAAAAAYRvAEAAAAAMIjgDQAAAACAQQRvAAAAAAAMypHgfeDAAYWGhiooKEjjx49XSkrKbdv88ssvatGihWrWrKlZs2ZlQ5UAAAAAANy9bA/e8fHx6tOnj3x9fbVw4UIdPnxY4eHht2wTExOjF154QS1bttT8+fO1dOlSbdmyJZsqBgAAAADgzmV78F6/fr1sNpuGDh2qMmXKaODAgVqwYMEt2yxZskRFixbViy++qHLlyqlv3763bQMAAAAAwL3AMbtXGBkZKX9/f7m4uEiSfHx8dPjw4Vu22b9/v2rVqiWLxSJJqlq1qiZNmpTpdf+3+T3Pt6S7XJytOV0GgAzyLuJq/zm37GeyRPGqklOBnK4CQGYUfsj+4/20v6rkWUkuji45XQaADCrnXs7+8728r8pMbdkevG02m7y8vOzDFotFDg4OunTpkjw8PNJtU6FCBfuwm5uboqKiMr3uwoULZr7gHDChg39OlwDgDhQq5Hr7mfKSNlNzugIAd+h+21+Nrjs6p0sAcAfy0r4q2081t1qtcnZ2TjMuX758iouLy3Cb280PAAAAAMC9ItuDt4eHh2JiYtKMi42NlZOTU4bb3G5+AAAAAADuFdkevP38/LRz50778IkTJxQfH5/uaeY3a7Nv3z4VK1bMaJ0AAAAAAGSFbA/eQUFBunLliiIiIiRJ06dPV506dWS1WmWz2ZSQkHBDm5CQEO3YsUNbtmxRYmKiPv/8cwUHB2dz5QAAAAAAZJ4lJSUlJbtX+uOPP2rQoEFydXVVUlKS5syZo4cfflghISEaNmyYmjRpckObuXPnaty4cXJzc1OBAgX07bffqkiRItldOgAAAAAAmZIjwVuSoqKitGvXLgUGBsrT0zNDbf766y8dPnxYNWrUkJubm+EKAQAAAAC4ezkWvAEAAAAAuB9k+zXeAAAAAADcTwjeAAAAAAAYRPBGrhQeHi4fHx/5+PjIz89P7du318aNG7N0HSdOnJCPj0+WLvOf/rkN/3xt3rzZ2DoBYOfOnWrfvr0CAgLUo0cPRUVFqVGjRpo/f759npSUFNWsWVOLFy/WkCFD5OPjowMHDkiSJkyYIB8fH23dujWnNgG474WHh6tNmzbGlh0WFpaly5wyZYqGDBmSJcsKCwuzHzPVrFlTr7zyimJiYrJk2dnJ9HEm7j0Eb+RajzzyiLZt26ZVq1apYcOG6t+/v2w2W06XlSmp2/DPV40aNXKkFv4AAHnf33//rb59+6pr16764Ycf5OrqqjfffFN16tTRL7/8Yp/vwIEDunjxourUqWMfd+jQoTT/AsibnnjiCX3yySeZanO7Y4jevXtr5MiRd1ua3cCBA/XLL7/oiy++0JkzZ/TOO+9k2bKzS8mSJbVt27acLgPZiOCNXMvBwUHu7u4qXry4+vfvr4SEBB05ciSny8qU1G3458vR0TGnywKQRx0+fFiXLl1SaGioSpQooX79+slisahu3bravn27fb7t27fLx8dHRYsWlSRZrVYdPHhQ0vXgbbVac6R+AOY5OzvL1dU1S5eZL18+ubi4ZOnyPDw8VKlSJXXp0kV79+7NsmVnl9RjQNw/CN7IE1auXClJKlGihKTrB41t2rSRv7+/QkND7adIbt26VSEhIVq9erUaNWqkoKAgffnll/blrFmzRo899phq1qypRYsWpVnHgQMH1KVLFz366KPq1auXzpw5I+n6KU9Dhw5VgwYNNGTIEL3xxhuqXr261qxZc1fbtG3bNrVp00ZBQUEaNGiQLl++bJ8WFham8PBwzZo1S40aNdLq1avt09avX69WrVqpevXqev311xUfH2+fNnPmTAUHByswMFADBw60T/Pz81Pjxo0lyX761u+//35X9QO495QoUUIWi0VTpkxRQkKCKlWqpKlTp6p27dqKjo7W8ePHJV3fh9atW9ferkqVKjp06JBsNpsSEhJUvHjxnNoEABlwq2OIn3/+WSEhIapXr54mTJigBg0apDmOSO9U87s5hkjvVPMvvvhCDRs2VM2aNTVy5EglJCRkajv//vtvrVmzRl5eXpKuXyYzY8YMNWrUSMHBwZo9e3aa+ceNG6fq1aurW7duGjp0qOrXry/pf8eHx44d07PPPqvOnTvb29hsNg0bNkw1a9ZUixYt0pwddPz4cXXr1k3VqlVT06ZNtX79evu03bt3q3379vL391fr1q21a9euNLWkd5ZAesebtzuGxb2P4I1c68CBA6pevbr8/f01YsQIjR49WkWKFFFycrJeeuklNW/eXD/++KMCAgI0YcIEe7sLFy7os88+06effqr+/ftrwoQJiouL07lz5zRgwAD16tVL3377rdatW2dvExsbq2eeeUZ169bVkiVLVKJECfXt21fJycmSpGPHjumNN97QokWL9Mgjj6hhw4Zp/ojdbhtSX/v375cknT59Wr1791bXrl0VHh6u2NjYG/5gzZ8/X1u2bNGYMWMUEBBgr6Nv377q3r27wsPDtWfPHs2YMUPS9Z6uiRMnavLkyVq0aJH++usvhYeHS5I2b96sxYsXS5L9lHc/P787/a8BcI8qXLiw3n33Xc2ePVvNmjVTRESEJKlQoUKqXLmyvdf738Hbx8dHBw8e1KFDh/TII4/kROkAMuhWxxApKSkaPHiwXn75Zb333nv65ptvNHv2bNWsWfOWyzRxDPH999/rs88+s9exbds2zZ07N0PbOGnSJFWvXl2PPvqojh8/rmHDhkmSFi9erOnTp2vSpEmaMmWK3n//fft+bePGjVq1apUWLlyohx9+WJcuXdLChQvty7x27Zr69eunJk2aaPjw4fbxb7/9tg4ePKhvv/1W3bt314svvqjY2Fh7HZ6enlqxYoV69eplr0OS3nzzTQUGBmrlypVq2rSpxo4de9vtut3xZnrHsMgdCN7ItcqXL6+IiAg98cQT8vPzU2hoqH3a4sWL1bNnT506dUqxsbE6evSofdrVq1c1atQoPfLII+rcubMSEhJ0/vx5bdiwQaVLl9aTTz6psmXLqn///vY2a9askaurq/r166dSpUpp+PDhOnr0qP744w9J16+HqlixoiTZ2ycmJmZ4G1Jf5cuXlyQtWbJEAQEBevLJJ1W6dGmNHj1aq1ev1tmzZ9Nsx9SpUxUcHCxPT09J0rJly1S5cmV16NBBZcqUUefOnfXTTz9Jun5aliTFx8erRIkS+u677/Tkk09KkgoWLCg3NzdJsp/yzqmkQN7UvHlzrVmzRu3atdOIESM0btw4SVLdunX1yy+/6K+//tKlS5dUvXp1e5sHH3xQV65c0Z49ewjewD3uVscQMTExio6OVosWLVSzZk25urrqwoUL9mOA9Jg4hli4cKG6d++u6tWry9vbW++++64CAwMztI3PPvuswsPDVbBgQT377LMqU6aMJCkiIkJPPvmkAgICFBAQoEaNGtmPg/bt26eAgACVLVtWISEhOnz4sP1yGkk6d+6cevfurS5duqhKlSqSpOTkZC1dulT9+/dX2bJl1blzZ+XLl88e5vPnz6+kpCRZLBZ17NhRa9euTfOeJSYmytnZWf369dPXX3992+263fFmesewyB0I3si1nJyc5OXlpe7du2vTpk06ceKEpOvXzMyaNUv169fX6NGjZbPZ7N8USpKHh4c9JDs7O0u6/g3w2bNn05w+Wbp0afvPp0+ftp/GlNruwQcf1OnTpyX97w/Sv3/O6DakvlLr+ff6ihUrJmdnZ/v6JKlz585ycnJKs7yoqCjt3bvX3oM+fvx4exsvLy+NHTtWEydOtN8F9MKFCxmuFUDuFxUVpWPHjqlgwYLq37+/PvvsM33xxRc6efKk/Trv7du369FHH1X+/PnTtK1QoYJWrlzJTRiBe9ytjiEeeOABubu76/fff9exY8d05coVlS1b9rbLNHEMcebMmTR1+vr6qmrVqhlq6+HhoTJlyqh9+/aaN2+efXxUVJS++uor+3HQqlWr7MdBZcuW1b59+3Tt2jXt3LlTDz30UJplFi5cWC1btkwzLiYmRvHx8XrllVfsy4yJidGpU6ckSQMGDFD+/PnVqlUrNWvWTMuWLbO3HT16tKKiotSkSRO1adNGmzZtuu123e54M71jWOQO3MUJud4jjzyigIAAfffddxowYIC2bt2qb775RqtWrVKRIkW0bt067dmzxz5/et/qenp6Kjo62j78z5BbsmRJe7CXrp+OFB0drZIlSxrYouvr27Jli304KipK8fHxadZ3s5uUFC9eXCEhIRo8eLCk69/U/v3335Ku/4Hz8fFReHi4Ll26pJdfflnTpk3TG2+8Ien6FxbS9R24xWIxsl0Actby5cu1evVqffXVV5KkoKAgOTk56cqVKwoICNC5c+e0dOlSBQcH39DWx8dHX375pYYOHZrdZQPIhFsdQyQnJ8vX11e9e/dWYmKi/vOf/9jPmrsVE8cQJUqUSHNstWTJEm3dulVvvfVWhre1c+fOat68uY4ePapy5cqpePHi6tChg5o1aybpeg99aidF2bJldf78eQUGBqpo0aL2S/FS5c+f/4baPT095ezsrOnTp9s7Z/7++28VLlxYkvTXX39p5MiRKliwoNasWaN+/frZz0Q8c+aMPvjgA1mtVs2bN08DBgzQtm3b7O/VzdzqeDMuLu62Zybg3kaPN/KELl26aOHChUpMTNTVq1clSVeuXNGOHTs0bty4DH0bWK9ePR05ckSLFi3SsWPHNGXKFPu0hg0bKjY2VlOnTtXJkyf11ltvqVy5csaug27durV+++03ffvttzp+/LhGjhypJk2aqEiRIrds98QTT2j79u3666+/JCnNQfKhQ4fUq1cv7dixQ3///bcsFkuaMwGKFi2qAgUK6KefftLJkye5uRqQB9WuXVu//fabli1bpqioKE2ZMkVFixaVt7e3nJ2dFRQUpJ9//jnN9d2pfHx85OTkJG9v7xyoHMC/JSYm6syZM2le8fHxtzyG2L59u/3a5rVr16pHjx4ZWpeJY4j27dtr9uzZ2r59uw4fPqyZM2faTxnPqLJly6p27dr69ttvJUlt27bV999/r9jYWMXFxWnEiBH2U7xnzpypsLAwLVmyRCtWrLihx/tmHBwc1Lp1a3333XeyWCw6ffq0unfvrp07d0qSJk6cqKlTp9p7wFPfEwcHBw0ePFizZ8+2XyaYkWPR7D7eRPYieCNPaNasmZKSkrR27VrVq1dPISEhat++vUaNGqUnn3xS0dHROnfu3C2XUaxYMfsO9KmnnkpznZGrq6tmzpypjRs3qlWrVjp16pSmTZt2y28t70bx4sX16aefau7cuWrXrp0KFChgvw7zVkqXLq133nlH77zzjp544gkdOHBAkyZNkiQFBwerU6dOevnll9WsWTOlpKSoT58+9rZOTk4aO3asRo0apZYtW+rHH380sm0Aco6Pj4/GjRunKVOmqHnz5tq6daumTZtmP2Wxbt26Kly4sP1Uxn+qWLGiypcvb58XQM46dOiQGjRokOa1d+/eWx5DVK1aVTExMXrqqafsdyifOnXqbddl4hiiZcuW6tWrlwYNGqSuXbuqRo0aeuaZZzL9PnTp0kWLFi2yf+nQokULPf/88+rcubO8vLz00ksvSZIee+wxzZgxQ6GhoapataqaNWtmD9C3MnToUDk4OKh9+/YaMGCAevbsqUaNGkm6fjr57t271bJlS40cOVJvvPGGvZNk4sSJ+uGHH9S0aVPNmjVL48aNu+1xY3YfbyJ7WVK4MAAAAADI895//31FRUXplVdekZOTkzZv3qwxY8Zo69atOV2acQ0aNNDo0aNVtWpVXbt2TePHj1fx4sVv+pgzwASu8QYAAADuA02aNNGbb76p5s2bKzExUeXKldOoUaNyuqxsERYWpjfffFPR0dHKnz+/qlevrqeffjqny8J9hB5vAAAAAAAM4oIBAAAAAAAMIngDAAAAAGAQwRsAAAAAAIMI3gAAAAAAGETwBgAAAADAIII3AAAAAAAGEbwBAAAAADCI4A0AAAAAgEEEbwAAAAAADPp/WD+doE/5gCMAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最佳模型: Random Forest (准确率: 0.8350)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest 详细性能分析:\n",
      "  旅行: 精确率=0.825, 召回率=0.776, F1-score=0.800, 样本数=85.0\n",
      "  运动: 精确率=0.513, 召回率=0.588, F1-score=0.548, 样本数=34.0\n",
      "  阅读: 精确率=1.000, 召回率=1.000, F1-score=1.000, 样本数=49.0\n",
      "  音乐: 精确率=1.000, 召回率=1.000, F1-score=1.000, 样本数=32.0\n",
      "\n",
      "随机森林特征重要性:\n",
      "  feature  importance\n",
      "1    收入水平    0.583756\n",
      "0      年龄    0.416244\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T10:55:52.675108Z",
     "start_time": "2025-11-25T10:55:52.439331Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 显示最佳模型的混淆矩阵\n",
    "best_model_predictions = predictions[best_model_name]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, best_model_predictions)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "           xticklabels=le_hobby.classes_,\n",
    "           yticklabels=le_hobby.classes_)\n",
    "plt.title(f'{best_model_name} - 混淆矩阵', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 特征重要性分析（对于树模型）\n",
    "if best_model_name == 'Random Forest':\n",
    "    best_model = models[best_model_name]\n",
    "    feature_importance = pd.DataFrame({\n",
    "        'feature': X.columns,\n",
    "        'importance': best_model.feature_importances_\n",
    "    }).sort_values('importance', ascending=False)\n",
    "\n",
    "    print(\"\\n随机森林特征重要性:\")\n",
    "    print(feature_importance)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(data=feature_importance, x='importance', y='feature')\n",
    "    plt.title('随机森林特征重要性')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ],
   "id": "d163967e2f585f5a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "随机森林特征重要性:\n",
      "  feature  importance\n",
      "0      年龄    0.428231\n",
      "1    收入水平    0.424977\n",
      "2    性别_男    0.146792\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-25T11:07:46.966906Z",
     "start_time": "2025-11-25T11:07:46.941062Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 预测示例\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"预测示例\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 选择几个测试样本进行预测展示\n",
    "sample_indices = np.random.choice(len(X_test), 5, replace=False)\n",
    "\n",
    "for i, idx in enumerate(sample_indices):\n",
    "    actual_hobby = le_hobby.inverse_transform([y_test.iloc[idx]])[0]\n",
    "\n",
    "    # 获取样本数据（保持DataFrame格式）\n",
    "    if best_model_name in ['SVM', 'Logistic Regression']:\n",
    "        # 对于需要标准化的模型，使用标准化后的数据但保持特征名称\n",
    "        sample_scaled = X_test_scaled[idx].reshape(1, -1)\n",
    "        predicted_hobby_encoded = models[best_model_name].predict(sample_scaled)[0]\n",
    "    else:\n",
    "        # 对于随机森林，使用原始的DataFrame格式\n",
    "        sample_df = X_test.iloc[idx:idx+1]  # 保持DataFrame格式\n",
    "        predicted_hobby_encoded = models[best_model_name].predict(sample_df)[0]\n",
    "\n",
    "    predicted_hobby = le_hobby.inverse_transform([predicted_hobby_encoded])[0]\n",
    "\n",
    "    # 获取原始特征值（从原始数据中）\n",
    "    original_idx = X_test.index[idx]\n",
    "    original_data = data.loc[original_idx]\n",
    "\n",
    "    print(f\"\\n样本 {i+1}:\")\n",
    "    print(f\"  年龄: {original_data['年龄']:.1f}\")\n",
    "    print(f\"  性别: {original_data['性别']}\")\n",
    "    print(f\"  收入水平: {original_data['收入水平']:.2f}\")\n",
    "    print(f\"  真实兴趣: {actual_hobby}\")\n",
    "    print(f\"  预测兴趣: {predicted_hobby}\")\n",
    "    print(f\"  预测结果: {'✓ 正确' if actual_hobby == predicted_hobby else '✗ 错误'}\")\n",
    "\n",
    "# 模型总结\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型总结\")\n",
    "print(\"=\"*60)\n",
    "print(f\"1. 使用的模型: {', '.join(models.keys())}\")\n",
    "print(f\"2. 最佳模型: {best_model_name}\")\n",
    "print(f\"3. 最佳准确率: {best_accuracy:.4f}\")\n",
    "print(f\"4. 分类类别: {list(le_hobby.classes_)}\")\n",
    "print(f\"5. 数据分布:\")\n",
    "hobby_counts = data['兴趣爱好'].value_counts()\n",
    "for hobby, count in hobby_counts.items():\n",
    "    print(f\"   - {hobby}: {count}个样本 ({count/len(data)*100:.1f}%)\")\n",
    "\n",
    "# 添加模型性能分析\n",
    "print(f\"\\n6. 模型性能分析:\")\n",
    "print(f\"   - 随机森林准确率: {results.get('Random Forest', 0):.4f}\")\n",
    "print(f\"   - SVM准确率: {results.get('SVM', 0):.4f}\")\n",
    "print(f\"   - 逻辑回归准确率: {results.get('Logistic Regression', 0):.4f}\")\n",
    "\n",
    "# 显示特征重要性总结（如果适用）\n",
    "if best_model_name == 'Random Forest':\n",
    "    print(f\"\\n7. 特征重要性分析:\")\n",
    "    feature_importance = pd.DataFrame({\n",
    "        'feature': X.columns,\n",
    "        'importance': models['Random Forest'].feature_importances_\n",
    "    }).sort_values('importance', ascending=False)\n",
    "\n",
    "    for _, row in feature_importance.iterrows():\n",
    "        feature_name = '年龄' if row['feature'] == '年龄' else '收入水平' if row['feature'] == '收入水平' else '性别'\n",
    "        print(f\"   - {feature_name}: {row['importance']:.3f}\")"
   ],
   "id": "37329942095467f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "预测示例\n",
      "============================================================\n",
      "\n",
      "样本 1:\n",
      "  年龄: 28.0\n",
      "  性别: nan\n",
      "  收入水平: 12457.62\n",
      "  真实兴趣: 音乐\n",
      "  预测兴趣: 音乐\n",
      "  预测结果: ✓ 正确\n",
      "\n",
      "样本 2:\n",
      "  年龄: 52.0\n",
      "  性别: nan\n",
      "  收入水平: 15038.00\n",
      "  真实兴趣: 旅行\n",
      "  预测兴趣: 运动\n",
      "  预测结果: ✗ 错误\n",
      "\n",
      "样本 3:\n",
      "  年龄: 20.0\n",
      "  性别: nan\n",
      "  收入水平: 10074.01\n",
      "  真实兴趣: 音乐\n",
      "  预测兴趣: 音乐\n",
      "  预测结果: ✓ 正确\n",
      "\n",
      "样本 4:\n",
      "  年龄: 26.0\n",
      "  性别: nan\n",
      "  收入水平: 14851.05\n",
      "  真实兴趣: 音乐\n",
      "  预测兴趣: 音乐\n",
      "  预测结果: ✓ 正确\n",
      "\n",
      "样本 5:\n",
      "  年龄: 27.0\n",
      "  性别: nan\n",
      "  收入水平: 16602.10\n",
      "  真实兴趣: 音乐\n",
      "  预测兴趣: 音乐\n",
      "  预测结果: ✓ 正确\n",
      "\n",
      "============================================================\n",
      "模型总结\n",
      "============================================================\n",
      "1. 使用的模型: Random Forest, SVM, Logistic Regression\n",
      "2. 最佳模型: Random Forest\n",
      "3. 最佳准确率: 0.8350\n",
      "4. 分类类别: ['旅行', '运动', '阅读', '音乐']\n",
      "5. 数据分布:\n",
      "   - 旅行: 423个样本 (42.3%)\n",
      "   - 阅读: 245个样本 (24.5%)\n",
      "   - 运动: 170个样本 (17.0%)\n",
      "   - 音乐: 162个样本 (16.2%)\n",
      "\n",
      "6. 模型性能分析:\n",
      "   - 随机森林准确率: 0.8350\n",
      "   - SVM准确率: 0.8050\n",
      "   - 逻辑回归准确率: 0.6550\n",
      "\n",
      "7. 特征重要性分析:\n",
      "   - 收入水平: 0.584\n",
      "   - 年龄: 0.416\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 三、\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 创建示例数据\n",
    "def create_sample_data():\n",
    "    np.random.seed(42)\n",
    "    n_samples = 1000\n",
    "\n",
    "    data = {\n",
    "        'brand': np.random.choice(['A', 'B', 'C', 'D'], n_samples),\n",
    "        'year': np.random.randint(2010, 2023, n_samples),\n",
    "        'mileage': np.random.randint(1000, 200000, n_samples),\n",
    "        'engine_size': np.random.uniform(1.0, 3.0, n_samples),\n",
    "        'fuel_type': np.random.choice(['Petrol', 'Diesel'], n_samples)\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    # 模拟价格计算\n",
    "    price = 50000 + \\\n",
    "            (df['brand'].map({'A': 10000, 'B': 5000, 'C': 0, 'D': -2000})) + \\\n",
    "            ((df['year'] - 2010) * 2000) + \\\n",
    "            (-df['mileage'] * 0.1) + \\\n",
    "            np.random.normal(0, 5000, n_samples)\n",
    "\n",
    "    df['price'] = price\n",
    "    return df\n",
    "\n",
    "# 数据预处理\n",
    "def preprocess_data(df):\n",
    "    df_processed = df.copy()\n",
    "\n",
    "    # 编码分类变量\n",
    "    categorical_cols = ['brand', 'fuel_type']\n",
    "    for col in categorical_cols:\n",
    "        le = LabelEncoder()\n",
    "        df_processed[col] = le.fit_transform(df_processed[col])\n",
    "\n",
    "    return df_processed\n",
    "\n",
    "# 主程序\n",
    "def main():\n",
    "    # 1. 加载数据\n",
    "    data = create_sample_data()\n",
    "    print(\"数据形状:\", data.shape)\n",
    "\n",
    "    # 2. 预处理\n",
    "    data_processed = preprocess_data(data)\n",
    "\n",
    "    # 3. 准备特征和目标\n",
    "    X = data_processed.drop('price', axis=1)\n",
    "    y = data_processed['price']\n",
    "\n",
    "    # 4. 分割数据\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # 5. 训练模型\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # 6. 预测和评估\n",
    "    y_pred = model.predict(X_val)\n",
    "    mae = mean_absolute_error(y_val, y_pred)\n",
    "\n",
    "    # 7. 输出结果\n",
    "    print(f\"验证集 MAE: {mae:.2f}\")\n",
    "    print(\"\\n特征重要性:\")\n",
    "    for feature, coef in zip(X.columns, model.coef_):\n",
    "        print(f\"{feature}: {coef:.2f}\")\n",
    "\n",
    "# 运行程序\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ],
   "id": "b150915e907de6f0"
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